More and more intelligent tasks rely on network edge nodes to collect data, which brings inevitable communication problems such as limited bandwidth and high data volume. As-sisted by deep learning, semantic communication technology is an effective means to improve the efficiency of joint communication and intelligent task execution. However, the commonly used global sampling-based pattern ignores the fact that the data processing ability of edge devices is strictly limited and only a small part of information is available at one time in some applications. This paper proposes a novel partial sampling based semantic communication framework where an edge device is guided by feedback from the receiver to locate and collect only part of the semantic content relevant to the target task at one time. Taking the vision-based task as an example, the transmitter selectively samples a small patch of a large-size image until the intelligent task is successfully executed at the receiver. Simulation results demonstrate that the proposed partial sampling-based framework can locate the informative areas accurately and achieve com-petitive performance compared to existing global sampling-based methods.


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    Titel :

    Partial Sampling-based Semantic Communications for Internet of Things


    Beteiligte:
    Yu, Kaiwen (Autor:in) / He, Qi (Autor:in) / Wu, Gang (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    551289 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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